code: def __init__(
      self,
      config,
      id_list,
      quantiles,
      n_forecasts,
      device,
      config_trend_none_bool,
  ):
      super().__init__(
          config=config,
          n_forecasts=n_forecasts,
          quantiles=quantiles,
          id_list=id_list,
          device=device,
          config_trend_none_bool=config_trend_none_bool,
      )
      if self.regressors_dims is not None:
          # Regresors params
          self.regressor_params = nn.ParameterDict(
              {
                  # dimensions - [no. of quantiles, no. of additive regressors]
                  "additive": init_parameter(
                      dims=[
                          len(self.quantiles),
                          self.n_additive_regressor_params,
                      ]
                  ),
                  # dimensions - [no. of quantiles, no. of multiplicative regressors]
                  "multiplicative": init_parameter(
                      dims=[
                          len(self.quantiles),
                          self.n_multiplicative_regressor_params,
                      ]
                  ),
              }
          )
  
file_path: neural_prophet/neuralprophet/components/future_regressors/linear.py
imports: import torch
  import torch.nn
  from neuralprophet.components.future_regressors import FutureRegressors
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 1 to create a new LinearFutureRegressors object from inherited base classes: FutureRegressors with example: obj = LinearFutureRegressors(config, id_list, quantiles, n_forecasts, device, config_trend_none_bool)